The present invention relates to computing technology, and particularly to techniques for automated bounding box generation for objects in an image. In one or more examples, such images with bounding boxes identified for objects in the images are used as training datasets.
Digital image based object detection has seen increased attention over the past few years. For example, object detection systems are currently being implemented in advanced driver assistance systems (ADAS), e-commerce applications, and various other areas. Conventional object detection methods usually involve two stages. First, in the detection stage, image regions that contain candidates of target objects are detected or localized. Then, in the recognition stage, such regions are further analyzed to recognize the specific content. However, these conventional object detection systems and methods generally require a large amount of training data, computing resources, have slow detection speeds, and can be inaccurate at times. Training data with bounding boxes for objects of interest is not easy to find and requires effort to generate.